Data Scientist – AI/ML Modeling

Job Description This role designs, validates, productizes custom machine learning and deep learning models for laboratory solutions. The Data Scientist will convert scientific workflow challenges into rigorous modeling problems, develop fit-for-purpose algorithms, establish defensible evaluation methods and partner with product, software, application science, quality and field teams to deliver reliable AI-enabled capabilities. The emphasis is on practical model development for laboratory data and scientific workflows—not on LLM or foundation-model specialization. Priority use cases may include chromatographic and mass spectrometry data analysis, image analysis, anomaly detection, model-assisted review and workflow intelligence. Key Responsibilities Convert AI product requirements, customer workflow pain points and scientific use cases into clear ML problem statements, model objectives, evaluation metrics and experimental plans. Design, train, validate and optimize custom ML/DL models for laboratory productivity use cases such as chromatography, mass spectrometry, image analysis, anomaly detection and model-assisted review. Select and adapt suitable architectures for scientific data, including convolutional, segmentation, temporal, graph-based, multimodal, or physics-informed approaches where appropriate. Build reproducible modeling workflows covering data curation, labeling strategy, ground-truth definition, feature engineering, quality checks, training, evaluation, and documentation. Work with application scientists and domain experts to design experiments and generate high-quality datasets for model development and benchmarking. Define performance metrics, statistical validation approaches, acceptance criteria, and evidence packages suitable for scientific and product decision-making. Translate prototypes into product-ready capabilities by collaborating with software engineering on model interfaces, inference workflows, APIs, performance constraints, deployment patterns, and lifecycle needs. Ensure model outputs are scientifically valid, explainable, reproducible and aligned with customer workflow expectations. Document assumptions, datasets, experiments, results, limitations, risks, and validation evidence to support quality review, responsible AI practices and maintainability. Monitor applied AI/ML research and selectively evaluate methods that can improve laboratory data analysis, automation and workflow intelligence with clear practical deployment value. Qualifications Qualifications Master’s degree or Ph.D. preferred in data science, machine learning, statistics, computer science, applied mathematics, bioinformatics, chemometrics, analytical chemistry, life sciences, or a related quantitative discipline. Minimum 5 years of relevant industry or applied research experience in machine learning, deep learning, data science, or scientific computing; advanced degree research experience may be considered. Strong hands-on Python experience with commonly used ML libraries and scientific computing tools such as PyTorch, TensorFlow, scikit-learn, NumPy, Pandas, or equivalent frameworks. Solid understanding of ML/DL fundamentals, including model selection, training strategy, validation design, optimization, evaluation metrics, and error analysis. Ability to structure ambiguous scientific or product problems into testable hypotheses, data plans, modeling approaches, experiments, and success criteria. Strong quantitative foundation in statistics, applied mathematics, optimization, signal processing, time-series analysis, computer vision, chemometrics, or adjacent methods for scientific data. Familiarity with reproducible ML practices, including experiment tracking, data traceability, model versioning, technical documentation and collaboration with software engineering teams. Fluent English and strong communication skills for technical documentation, experiment reviews, and collaboration with global stakeholders. Preferred Experience Applied AI/ML experience in laboratory, life science, analytical instrumentation, scientific workflow, or customer-facing product environments. Domain exposure to chromatography, mass spectrometry, peak integration, peak detection, baseline correction, spectral analysis, scientific imaging, or instrument-generated signal data. Experience tailoring advanced model architectures to noisy, limited, heterogeneous, imbalanced, or instrument-dependent scientific datasets. Practical experience improving model robustness, explainability, uncertainty handling, or performance consistency across real-world operating conditions. Experience transitioning models beyond proof of concept, including inference design, model serving, APIs, containerization, CI/CD integration, monitoring, or lifecycle management. Exposure to LLMs, foundation models, or AI-assisted software development tools is a plus, but not a core requirement. Additional Details This job has a full time weekly schedule. Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: Agilent Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws. Travel Required: Occasional Shift: Day Duration: No End Date Job Function: R&D